Lesion Segmentation in Paediatric Epilepsy Utilizing Deep Learning Approaches

Focal cortical dysplasia (FCD) is one of the most common lesions responsible for drug-resistant epilepsy, and is frequently missed by visual inspection. FCD may be amenable to surgical resection to achieve seizure freedom. By improving lesion detection the surgical outcome of these patients can be improved. Image processing techniques are a potential tool to improve the detection of FCD prior to epilepsy surgery. In this research, we propose and compare the performance of two type of models, Fully Convolutional Network (FCN) and a multi-sequence FCN to classify and segment FCD in children with drug-resistant epilepsy. This experiment utilized the volumetric T1-weighted, T2 weighted and FLAIR sequences. The whole slice FCN models were applied to each sequence separately while the multi-sequence model leverages combined information of all three sequences simultaneously. A leave-one-subject-out technique was utilized to train and evaluate the models. We evaluated subjectwise sensitivity and specificity, which corresponds to the ability of the model to classify those with or without a lesion. We also evaluated lesional sensitivity and specificity, which expresses the ability of the model to segment the lesion and the dice coefficient to evaluate lesion coverage. Our data consisted of 80 FCD subjects (56 MR-positive and 24 MR-negative) and 15 healthy controls. Performance of whole slice FCN was best on T1-weighted, followed by T2-weighted and lowest with FLAIR sequences. Multi-sequence model performed better than the T1 whole slice FCN, and detected 98% vs. 93% respectively MR-positive cases, and 92% vs. 88% respectively MR-negative cases, as well as achieved lesion coverage of 74% vs. 67% respectively for MR-positive cases and 68% vs. 64% for MR negative cases. The dice coefficient for the multi-sequence model was 57% and for whole slice FCN was 56% for MR-positive cases. In the test cohort of six new cases, the multi-sequence model detected 4 out of 6 cases where the predicted lesion had 56% overlap with the actual lesion. This work showed that deep learning methods in particular fully convolutional networks are a promising tool for classification and segmentation of FCD. Additional work is required to further improve lesion classification and segmentation, particularly for small lesions, as well as to train and test optimal algorithms on a larger multi-center dataset.